Change reduce_bn to distribute_bn, add ability to choose between broadcast and reduce (mean). Add crop_pct arg to allow selecting validation crop while training.

timm/utils.py

@@ -210,12 +210,17 @@ def reduce_tensor(tensor, n):
     return rt
 
 
-def reduce_bn(model, world_size):
+def distribute_bn(model, world_size, reduce=False):
     # ensure every node has the same running bn stats
     for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True):
         if ('running_mean' in bn_name) or ('running_var' in bn_name):
-            torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
+            if reduce:
-            bn_buf /= float(world_size)
+                # average bn stats across whole group
+                torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
+                bn_buf /= float(world_size)
+            else:
+                # broadcast bn stats from rank 0 to whole group
+                torch.distributed.broadcast(bn_buf, 0)
 
 
 class ModelEma:

train.py

@@ -55,6 +55,8 @@ parser.add_argument('--gp', default='avg', type=str, metavar='POOL',
                     help='Type of global pool, "avg", "max", "avgmax", "avgmaxc" (default: "avg")')
 parser.add_argument('--img-size', type=int, default=None, metavar='N',
                     help='Image patch size (default: None => model default)')
+parser.add_argument('--crop-pct', default=None, type=float,
+                    metavar='N', help='Input image center crop percent (for validation only)')
 parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
                     help='Override mean pixel value of dataset')
 parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
@@ -121,6 +123,10 @@ parser.add_argument('--bn-momentum', type=float, default=None,
                     help='BatchNorm momentum override (if not None)')
 parser.add_argument('--bn-eps', type=float, default=None,
                     help='BatchNorm epsilon override (if not None)')
+parser.add_argument('--sync-bn', action='store_true',
+                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
+parser.add_argument('--dist-bn', type=str, default='',
+                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
 # Model Exponential Moving Average
 parser.add_argument('--model-ema', action='store_true', default=False,
                     help='Enable tracking moving average of model weights')
@@ -143,10 +149,6 @@ parser.add_argument('--save-images', action='store_true', default=False,
                     help='save images of input bathes every log interval for debugging')
 parser.add_argument('--amp', action='store_true', default=False,
                     help='use NVIDIA amp for mixed precision training')
-parser.add_argument('--sync-bn', action='store_true',
-                    help='enabling apex sync BN.')
-parser.add_argument('--reduce-bn', action='store_true',
-                    help='average BN running stats across all distributed nodes between train and validation.')
 parser.add_argument('--no-prefetcher', action='store_true', default=False,
                     help='disable fast prefetcher')
 parser.add_argument('--output', default='', type=str, metavar='PATH',
@@ -349,6 +351,7 @@ def main():
         std=data_config['std'],
         num_workers=args.workers,
         distributed=args.distributed,
+        crop_pct=data_config['crop_pct'],
     )
 
     if args.mixup > 0.:
@@ -390,16 +393,16 @@ def main():
                 lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir,
                 use_amp=use_amp, model_ema=model_ema)
 
-            if args.distributed and args.reduce_bn:
+            if args.distributed and args.dist_bn and args.dist_bn in ('broadcast', 'reduce'):
                 if args.local_rank == 0:
-                    logging.info("Averaging bn running means and vars")
+                    logging.info("Distributing BatchNorm running means and vars")
-                reduce_bn(model, args.world_size)
+                distribute_bn(model, args.world_size, args.dist_bn == 'reduce')
 
             eval_metrics = validate(model, loader_eval, validate_loss_fn, args)
 
             if model_ema is not None and not args.model_ema_force_cpu:
                 if args.distributed and args.reduce_bn:
-                    reduce_bn(model_ema, args.world_size)
+                    distribute_bn(model_ema, args.world_size)
 
                 ema_eval_metrics = validate(
                     model_ema.ema, loader_eval, validate_loss_fn, args, log_suffix=' (EMA)')